如何使张量流模型将列表作为输入？

Question

I'm new with tensorflow, and I'm making an AI that does multiplication,
and I need to make it so that my model can take lists as input.

Here is my code:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

multiplication_q = np.array([[10,10],[1,1],[2,2],[0,0],[3,3],[4,4],[5,5],[6,6],[7,7],[8,8],[9,9],[1,0],[11,10],[27,0],[30,2],[4,3],[17,22],[20,0],[8,13],[21,4],[19,24],[11,19],[8,2],[4,5],[11,11],[1,15],[2,12],[15,3],[18,0],[49,7],[5,7],[12,4]], dtype=object)
multiplication_a = np.array([100,1,4,0,9,16,25,36,49,64,96,0,110,0,60,12,374,0,104,84,456,209,16,20,121,15,24,45,0,343,35,48], dtype=float)


model = tf.keras.Sequential([
  tf.keras.layers.Dense(units=4, input_shape=[1]),
  tf.keras.layers.Dense(units=4),
  tf.keras.layers.Dense(units=1)
])

model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam(0.1))

history = model.fit(multiplication_q, multiplication_a, epochs=750, verbose=False)

print(model.predict([4, 5]))

and here is the error message:

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
        y_pred = self(x, training=True)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__
        self.name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility
        ' but received input with shape ' + str(shape))

    ValueError: Input 0 of layer sequential_10 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape [32, 2]

Answer 1

To fix your issue you should do 3 things:

1- Change the dtype in the multiplication_q from object to int like this:

multiplication_q = np.array([[10,10],[1,1],[2,2],[0,0],[3,3],[4,4],[5,5],[6,6],[7,7],[8,8],[9,9],[1,0],[11,10],[27,0],[30,2],[4,3],[17,22],[20,0],[8,13],[21,4],[19,24],[11,19],[8,2],[4,5],[11,11],[1,15],[2,12],[15,3],[18,0],[49,7],[5,7],[12,4]], dtype=int)

2- And in the first Dense layer of your model use input_shape=(2,) instead of input_shape=[1] , like this:

model = tf.keras.Sequential([
  tf.keras.layers.Dense(units=4, input_shape=(2,)),
  tf.keras.layers.Dense(units=4),
  tf.keras.layers.Dense(units=1)
])

3- And for the predict function you should pass a list of list and not a list , cause you did a training with list of list

model.predict([[4, 5]])

Answer 2

Try setting your input in the first dense layer to multiplication_q.shape , you set your input shape to be 1 while your input is shaped 32, 2

EDIT: The code below resolved your issue although you will have to play around with stuff because it is not very accurate.

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

multiplication_q = np.asarray([[10,10],[1,1],[2,2],[0,0],[3,3],[4,4],[5,5],[6,6],[7,7],[8,8],[9,9],[1,0],[11,10],[27,0],[30,2],[4,3],[17,22],[20,0],[8,13],[21,4],[19,24],[11,19],[8,2],[4,5],[11,11],[1,15],[2,12],[15,3],[18,0],[49,7],[5,7],[12,4]])
multiplication_a = np.asarray([100,1,4,0,9,16,25,36,49,64,96,0,110,0,60,12,374,0,104,84,456,209,16,20,121,15,24,45,0,343,35,48])


multiplication_q = multiplication_q/np.amax(multiplication_q)
multiplication_a = multiplication_a/np.amax(multiplication_a)


model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(2, )))
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dense(units=1))


model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam(0.1))

history = model.fit(multiplication_q, multiplication_a, epochs=750)

print(model.predict(np.asarray([[4, 5]])/np.amax(multiplication_q)*np.amax(multiplication_a)))